Towards conservative inference in credal networks using belief functions: the case of credal chains

TL;DR

This paper introduces a new belief inference framework for credal chains using Dempster-Shafer theory, offering efficient and conservative uncertainty propagation with practical insights into its advantages and limitations.

Contribution

It formalizes belief-based inference methods for credal chains and compares them with classical sensitivity analysis, advancing uncertainty propagation techniques.

Findings

Belief inference provides conservative uncertainty intervals.

The approach balances computational efficiency with robustness.

Numerical results demonstrate practical utility and limitations.

Abstract

This paper explores belief inference in credal networks using Dempster-Shafer theory. By building on previous work, we propose a novel framework for propagating uncertainty through a subclass of credal networks, namely chains. The proposed approach efficiently yields conservative intervals through belief and plausibility functions, combining computational speed with robust uncertainty representation. Key contributions include formalizing belief-based inference methods and comparing belief-based inference against classical sensitivity analysis. Numerical results highlight the advantages and limitations of applying belief inference within this framework, providing insights into its practical utility for chains and for credal networks in general.